A hybrid boundary detection algorithm based on watershed and snake

Abstract

Watersheds and snakes are used extensively in image processing, particularly in locating object boundaries. Both of them have their own advantages and limitations. Watersheds can provide accurate and closed contours with single pixel width. The main problem with watersheds is over-segmentation. Snakes, or active contour, can locate desired object boundaries automatically and dynamically from an initial contour, but the narrow capture range of initial contour has limited their utility. This paper presents a hybrid boundary detection algorithm to incorporate the advantages of both watersheds and snakes. An initial contour is firstly transformed into the maximum watershed contour it contains. The later are then input to the snake model and begins its evolvement to the interested object boundary. In our experiments, we compared this hybrid algorithm with traditional snake and gradient vector flow (GVF) snake. The results show that the hybrid scheme has larger capture range, faster calculation and robustness to noise.